Ontology
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Agentic Retrieval of Topics and Insights from Earnings Calls
2025-07-10 16:38:59Anant Gupta, Rajarshi Bhowmik, Geoffrey GunowAbstract
Tracking the strategic focus of companies through topics in their earnings calls is a key task in financial analysis. However, as industries evolve, traditional topic modeling techniques struggle to dynamically capture emerging topics and their relationships. In this work, we propose an LLM-agent driven approach to discover and retrieve emerging topics from quarterly earnings calls. We propose an LLM-agent to extract topics from documents, structure them into a hierarchical ontology, and establish relationships between new and existing topics through a topic ontology. We demonstrate the use of extracted topics to infer company-level insights and emerging trends over time. We evaluate our approach by measuring ontology coherence, topic evolution accuracy, and its ability to surface emerging financial trends.
Abstract (translated)
通过分析公司财报电话会议中的主题来追踪公司的战略重点是财务分析的一项关键任务。然而，随着行业的演变，传统的主题建模技术难以动态捕捉新兴话题及其相互关系。为此，我们提出了一种基于大型语言模型（LLM）代理的创新方法，用于从季度财报电话会议中发现和检索新兴话题。 我们的方法包括使用一个由大型语言模型驱动的代理来执行以下任务： 1. 从文档中提取主题。 2. 将这些主题结构化为层次化的本体模型。 3. 建立新旧话题之间的关系，通过构建主题本体的方式进行整合和更新。 我们展示了如何利用提取的主题来推断公司的层面见解，并随着时间的推移识别出新兴趋势。为了评估这种方法的有效性，我们将从以下几个方面来进行衡量： - 本体的一致性和连贯性。 - 主题演变的准确性。 - 发现新金融趋势的能力。 通过这些方式，我们旨在提供一种更灵活、适应性强的方法来捕捉和理解公司战略方向的变化以及行业动态。
URL
https://arxiv.org/abs/2507.07906
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An Integrated Framework of Prompt Engineering and Multidimensional Knowledge Graphs for Legal Dispute Analysis
2025-07-10 16:22:41Mingda Zhang, Na Zhao, Jianglong Qing, Qing xu, Kaiwen Pan, Ting luoAbstract
The rapid development of artificial intelligence has positioned large language models as fundamental components of intelligent legal systems. However, these models face significant limitations in legal dispute analysis, including insufficient legal knowledge representation, limited concept understanding, and reasoning deficiencies. This research proposes an enhanced framework integrating prompt engineering with multidimensional knowledge graphs. The framework introduces a three-stage hierarchical prompt structure comprising task definition, knowledge background, and reasoning guidance, supplemented by legal-specific reasoning templates and dynamic optimization mechanisms. A three-layer knowledge graph architecture is constructed with legal classification ontology, representation, and instance layers. Four complementary methods enable precise legal concept retrieval: direct legal norm code matching, domain-specific semantic vector similarity, ontology-based path reasoning, and specialized lexical segmentation. These components integrate with web search technology to establish a knowledge-enhanced framework for legal decision-making. Experimental results demonstrate significant performance improvements in legal dispute analysis, enabling accurate legal application analysis for complex cases while exhibiting nuanced understanding of judicial decision-making logic, providing a novel technical approach for implementing intelligent legal assistance systems.
Abstract (translated)
人工智能的快速发展已将大型语言模型定位为智能法律系统中的核心组成部分。然而，这些模型在法律纠纷分析方面面临显著限制，包括法律知识表示不足、概念理解有限以及推理能力欠缺等问题。本研究提出了一种改进框架，该框架结合了提示工程与多维度知识图谱技术。这一框架引入了一个三阶段层次化提示结构，涵盖任务定义、知识背景和推理指导，并辅以特定于法律的推理模板及动态优化机制。同时构建了一个三层知识图架构，包括法律分类本体论层、表示层以及实例层。为了实现精确的法律概念检索，提出了四种互补方法：直接匹配法律规定代码、领域特定语义向量相似度分析、基于本体论路径的推理以及专业词汇分割。这些组件与网络搜索技术结合使用，以建立一个增强知识支持的法律决策框架。 实验结果表明，在法律纠纷分析方面性能有了显著提升，能够对复杂案件进行准确的应用分析，并展现出对司法判决逻辑的细微理解能力，为智能法律辅助系统的实施提供了新颖的技术路径。
URL
https://arxiv.org/abs/2507.07893
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Bridging Perception and Language: A Systematic Benchmark for LVLMs' Understanding of Amodal Completion Reports
2025-07-08 09:06:47Amane Watahiki, Tomoki Doi, Taiga Shinozaki, Satoshi Nishida, Takuya Niikawa, Katsunori Miyahara, Hitomi YanakaAbstract
One of the main objectives in developing large vision-language models (LVLMs) is to engineer systems that can assist humans with multimodal tasks, including interpreting descriptions of perceptual experiences. A central phenomenon in this context is amodal completion, in which people perceive objects even when parts of those objects are hidden. Although numerous studies have assessed whether computer-vision algorithms can detect or reconstruct occluded regions, the inferential abilities of LVLMs on texts related to amodal completion remain unexplored. To address this gap, we constructed a benchmark grounded in Basic Formal Ontology to achieve a systematic classification of amodal completion. Our results indicate that while many LVLMs achieve human-comparable performance overall, their accuracy diverges for certain types of objects being completed. Notably, in certain categories, some LLaVA-NeXT variants and Claude 3.5 Sonnet exhibit lower accuracy on original images compared to blank stimuli lacking visual content. Intriguingly, this disparity emerges only under Japanese prompting, suggesting a deficiency in Japanese-specific linguistic competence among these models.
Abstract (translated)
开发大型视觉语言模型（LVLM）的主要目标之一是创建能够协助人类完成多模态任务的系统，包括解读感知经验的描述。在这个背景下，一个核心现象是超模态完形，即人们即使在部分物体被遮挡的情况下也能感知到整个对象的存在。尽管许多研究已经评估了计算机视觉算法是否能检测或重建被遮挡区域，但关于LVLM在其文本推理能力中与超模态完形相关的表现仍缺乏探索。为了填补这一空白，我们基于基本形式本体论构建了一个基准测试，以系统地分类超模态完形现象。我们的结果显示，尽管许多LVLM整体上达到了接近人类的性能水平，但在某些类型的被完成对象类别上它们的准确性却存在差异。值得注意的是，在某些类别中，一些LLaVA-NeXT变种和Claude 3.5 Sonnet模型在原始图片上的准确率低于缺少视觉内容的空白刺激物。有趣的是，这种差距仅在日本语提示下出现，表明这些模型在日语特定的语言能力上存在不足。
URL
https://arxiv.org/abs/2507.05799
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RELRaE: LLM-Based Relationship Extraction, Labelling, Refinement, and Evaluation
2025-07-04 22:27:06George Hannah, Jacopo de Berardinis, Terry R. Payne, Valentina Tamma, Andrew Mitchell, Ellen Piercy, Ewan Johnson, Andrew Ng, Harry Rostron, Boris KonevAbstract
A large volume of XML data is produced in experiments carried out by robots in laboratories. In order to support the interoperability of data between labs, there is a motivation to translate the XML data into a knowledge graph. A key stage of this process is the enrichment of the XML schema to lay the foundation of an ontology schema. To achieve this, we present the RELRaE framework, a framework that employs large language models in different stages to extract and accurately label the relationships implicitly present in the XML schema. We investigate the capability of LLMs to accurately generate these labels and then evaluate them. Our work demonstrates that LLMs can be effectively used to support the generation of relationship labels in the context of lab automation, and that they can play a valuable role within semi-automatic ontology generation frameworks more generally.
Abstract (translated)
在实验室中，机器人执行的实验产生了大量的XML数据。为了支持不同实验室之间的数据互操作性，将XML数据转换为知识图的需求日益增加。此过程中关键的一个阶段是丰富XML模式以奠定本体（ontology）模式的基础。为此，我们提出了RELRaE框架，这是一个利用大型语言模型在不同阶段提取和准确标记XML模式中隐含关系的框架。我们的研究探讨了LLM生成这些标签的能力，并对其进行评估。 研究表明，大型语言模型可以在实验室自动化环境中有效支持关系标签的生成，并且它们可以更广泛地在半自动本体生成框架中发挥重要作用。
URL
https://arxiv.org/abs/2507.03829
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A Comparative Study of Competency Question Elicitation Methods from Ontology Requirements
2025-07-01 14:49:30Reham Alharbi, Valentina Tamma, Terry R. Payne, Jacopo de BerardinisAbstract
Competency Questions (CQs) are pivotal in knowledge engineering, guiding the design, validation, and testing of ontologies. A number of diverse formulation approaches have been proposed in the literature, ranging from completely manual to Large Language Model (LLM) driven ones. However, attempts to characterise the outputs of these approaches and their systematic comparison are scarce. This paper presents an empirical comparative evaluation of three distinct CQ formulation approaches: manual formulation by ontology engineers, instantiation of CQ patterns, and generation using state of the art LLMs. We generate CQs using each approach from a set of requirements for cultural heritage, and assess them across different dimensions: degree of acceptability, ambiguity, relevance, readability and complexity. Our contribution is twofold: (i) the first multi-annotator dataset of CQs generated from the same source using different methods; and (ii) a systematic comparison of the characteristics of the CQs resulting from each approach. Our study shows that different CQ generation approaches have different characteristics and that LLMs can be used as a way to initially elicit CQs, however these are sensitive to the model used to generate CQs and they generally require a further refinement step before they can be used to model requirements.
Abstract (translated)
能力问题（CQ）在知识工程中至关重要，它们指导本体的设计、验证和测试。文献中提出了一系列不同的表述方法，从完全手动到由大型语言模型（LLM）驱动的方法都有涵盖。然而，对这些方法的输出进行特征化及其系统性比较的研究却很少。本文通过实证研究对比了三种不同的CQ表述方法：由本体工程师手工编写、根据CQ模式实例化和利用最新技术的LLM生成这三种方式。我们从文化遗产生命周期的需求中使用每种方法生成CQ，并在多个维度对其进行评估：接受度程度、模糊性、相关性、可读性和复杂性。我们的贡献有两点： (i) 一个由多注释者共同参与，基于同一来源但通过不同方法生成的CQ数据集； (ii) 对于每种方法产生的CQ特性进行系统的比较。 我们的研究表明，不同的CQ生成方法具有各自的特点，并且LLM可以作为一种初步激发CQ的方式。然而，这些CQ对于所使用的模型敏感，通常需要进一步的细化步骤后才能用于建模需求。
URL
https://arxiv.org/abs/2507.02989
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Generating Reliable Adverse event Profiles for Health through Automated Integrated Data : A Semi-Automated Ontology Building Approach
2025-06-25 21:48:21Srikar Reddy Gadusu, Larry Callahan, Samir Lababidi, Arunasri Nishtala, Sophia Healey, Hande McGintyAbstract
As data and knowledge expand rapidly, adopting systematic methodologies for ontology generation has become crucial. With the daily increases in data volumes and frequent content changes, the demand for databases to store and retrieve information for the creation of knowledge graphs has become increasingly urgent. The previously established Knowledge Acquisition and Representation Methodology (KNARM) outlines a systematic approach to address these challenges and create knowledge graphs. However, following this methodology highlights the existing challenge of seamlessly integrating Neo4j databases with the Web Ontology Language (OWL). Previous attempts to integrate data from Neo4j into an ontology have been discussed, but these approaches often require an understanding of description logics (DL) syntax, which may not be familiar to many users. Thus, a more accessible method is necessary to bridge this gap. This paper presents a user-friendly approach that utilizes Python and its rdflib library to support ontology development. We showcase our novel approach through a Neo4j database we created by integrating data from the Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) database. Using this dataset, we developed a Python script that automatically generates the required classes and their axioms, facilitating a smoother integration process. This approach offers a practical solution to the challenges of ontology generation in the context of rapidly growing adverse drug event datasets, supporting improved drug safety monitoring and public health decision-making.
Abstract (translated)
随着数据和知识的迅速增长，采用系统化的本体生成方法变得至关重要。由于每天的数据量不断增加以及内容频繁变化，数据库需要存储和检索用于创建知识图的信息的需求变得更加紧迫。此前建立的知识获取与表示方法（KNARM）概述了一种系统化的方法来应对这些挑战并创建知识图。然而，遵循此方法时会遇到现有挑战之一：如何无缝地将Neo4j数据库与Web本体语言（OWL）集成。之前尝试从Neo4j中整合数据到本体中的方法已经被讨论过，但这些方法通常需要对描述逻辑（DL）语法有了解，而这对于许多用户来说可能不熟悉。因此，有必要提供一种更易于使用的方法来弥合这一差距。 本文介绍了一种利用Python及其rdflib库支持本体现代化开发的用户友好方法。我们通过将来自美国食品药品监督管理局不良事件报告系统（FAERS）数据库的数据整合到一个Neo4j数据库中，展示了我们的新方法。在此数据集上，我们开发了一个Python脚本，该脚本能自动生成所需的类及其公理，从而简化了集成过程。 这种方法为在快速发展的不良药物事件数据集中生成本体的挑战提供了一种实用解决方案，支持改进药物安全监控和公共健康决策制定。
URL
https://arxiv.org/abs/2506.20851
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Evaluating Rare Disease Diagnostic Performance in Symptom Checkers: A Synthetic Vignette Simulation Approach
2025-06-24 16:06:37Takashi Nishibayashi, Seiji Kanazawa, Kumpei YamadaAbstract
Background: Symptom Checkers (SCs) provide users with personalized medical information. To prevent performance degradation from algorithm updates, SC developers must evaluate diagnostic performance changes for individual diseases before deployment. However, acquiring sufficient evaluation data for rare diseases is difficult, and manually creating numerous clinical vignettes is costly and impractical. Objective: This study proposes and validates a novel Synthetic Vignette Simulation Approach to evaluate diagnostic performance changes for individual rare diseases following SC algorithm updates. Methods: We used disease-phenotype annotations from the Human Phenotype Ontology (HPO), a knowledge database for rare diseases, to generate synthetic vignettes. With these, we simulated SC interviews to estimate the impact of algorithm updates on real-world diagnostic performance. The method's effectiveness was evaluated retrospectively by comparing estimated values with actual metric changes using the R 2(R-squared) coefficient. Results: The experiment included eight past SC algorithm updates. For updates on diseases with frequency information in HPO (n=5), the R^2 for recall@8 change was 0.831 (p=0.031), and for precision@8 change, it was 0.78 (p=0.047), indicating the method can predict post-deployment performance. In contrast, large prediction errors occurred for diseases without frequency information (n=3), highlighting its importance. The manual effort to map HPO phenotypes to SC symptoms was approximately 2 hours per disease. Conclusions: Our method enables pre-deployment evaluation of SC algorithm changes for individual rare diseases using a publicly available, expert-created knowledge base. This transparent and low-cost approach allows developers to efficiently improve diagnostic performance for rare diseases, potentially enhancing support for early diagnosis.
Abstract (translated)
背景：症状检查器（SC）为用户提供个性化医疗信息。为了避免因算法更新导致性能下降，SC 开发人员必须在部署前评估针对各种疾病诊断性能的变化情况。然而，获取罕见病的足够评价数据非常困难，并且手动创建大量的临床案例描述既耗时又不切实际。目标：本研究提出并验证了一种新的合成病例模拟方法（Synthetic Vignette Simulation Approach），用于在SC算法更新后评估个体罕见疾病的诊断性能变化情况。 方法：我们使用人类表型本体论（HPO）中的疾病-表型注释，这是一个罕见病知识库，来生成合成病例。利用这些合成病例，我们模拟了 SC 与用户的互动，以估计算法更新对实际世界中诊断性能的影响。通过将预测值和实际度量变化进行比较，使用 R^2（决定系数）评估该方法的有效性。 结果：实验包括了八个过去的SC算法更新。对于在HPO中有频率信息的疾病更新（n=5），R^2 用于 recall@8 的变化为0.831 (p=0.031)，用于 precision@8 变化的 R^2 为0.78 (p=0.047)，表明该方法能够预测部署后的性能。相比之下，对于没有频率信息的疾病（n=3），其预测误差较大，这突显了频率信息的重要性。将 HPO 表型映射到 SC 症状的手动工作量约为每种疾病2小时。 结论：我们的方法允许使用公开且由专家创建的知识库，在部署前对罕见病个体的SC算法变化进行评估。这种透明度高、成本低的方法使开发人员能够有效地改善罕见疾病的诊断性能，从而有助于早期诊断的支持。
URL
https://arxiv.org/abs/2506.19750
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Ontology Neural Network and ORTSF: A Framework for Topological Reasoning and Delay-Robust Control
2025-06-24 03:14:51Jaehong OhAbstract
The advancement of autonomous robotic systems has led to impressive capabilities in perception, localization, mapping, and control. Yet, a fundamental gap remains: existing frameworks excel at geometric reasoning and dynamic stability but fall short in representing and preserving relational semantics, contextual reasoning, and cognitive transparency essential for collaboration in dynamic, human-centric environments. This paper introduces a unified architecture comprising the Ontology Neural Network (ONN) and the Ontological Real-Time Semantic Fabric (ORTSF) to address this gap. The ONN formalizes relational semantic reasoning as a dynamic topological process. By embedding Forman-Ricci curvature, persistent homology, and semantic tensor structures within a unified loss formulation, ONN ensures that relational integrity and topological coherence are preserved as scenes evolve over time. The ORTSF transforms reasoning traces into actionable control commands while compensating for system delays. It integrates predictive and delay-aware operators that ensure phase margin preservation and continuity of control signals, even under significant latency conditions. Empirical studies demonstrate the ONN + ORTSF framework's ability to unify semantic cognition and robust control, providing a mathematically principled and practically viable solution for cognitive robotics.
Abstract (translated)
自主机器人系统的进步已经在感知、定位、地图构建和控制方面取得了显著的成就。然而，仍存在一个根本性的缺口：现有的框架在几何推理和动态稳定性方面表现出色，但在表示和保持关系语义、情境推理以及认知透明度方面却表现不足——这些都是在动态的人类中心环境中进行协作所必需的条件。本文介绍了一种统一架构，包括本体神经网络（Ontology Neural Network，ONN）和本体实时语义织物（Ontological Real-Time Semantic Fabric，ORTSF），以解决这一缺口。 **ONN** 将关系语义推理形式化为一个动态拓扑过程。通过在统一损失函数中嵌入Forman-Ricci曲率、持久同调性和语义张量结构，ONN确保了随着场景的发展，关系完整性和拓扑一致性得以保持。 **ORTSF** 则将推理轨迹转化为可执行的控制命令，并补偿系统延迟。它整合了预测和时延感知操作符，以保证在显著延迟条件下仍然能够保持相位裕度并实现控制信号的连续性。 实证研究表明，ONN与ORTSF框架具备统一语义认知和鲁棒控制的能力，为认知机器人提供了一个既具有数学原理又实际可行的解决方案。
URL
https://arxiv.org/abs/2506.19277
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medicX-KG: A Knowledge Graph for Pharmacists' Drug Information Needs
2025-06-22 09:28:48Lizzy Farrugia, Lilian M. Azzopardi, Jeremy Debattista, Charlie AbelaAbstract
The role of pharmacists is evolving from medicine dispensing to delivering comprehensive pharmaceutical services within multidisciplinary healthcare teams. Central to this shift is access to accurate, up-to-date medicinal product information supported by robust data integration. Leveraging artificial intelligence and semantic technologies, Knowledge Graphs (KGs) uncover hidden relationships and enable data-driven decision-making. This paper presents medicX-KG, a pharmacist-oriented knowledge graph supporting clinical and regulatory decisions. It forms the semantic layer of the broader medicX platform, powering predictive and explainable pharmacy services. medicX-KG integrates data from three sources, including, the British National Formulary (BNF), DrugBank, and the Malta Medicines Authority (MMA) that addresses Malta's regulatory landscape and combines European Medicines Agency alignment with partial UK supply dependence. The KG tackles the absence of a unified national drug repository, reducing pharmacists' reliance on fragmented sources. Its design was informed by interviews with practicing pharmacists to ensure real-world applicability. We detail the KG's construction, including data extraction, ontology design, and semantic mapping. Evaluation demonstrates that medicX-KG effectively supports queries about drug availability, interactions, adverse reactions, and therapeutic classes. Limitations, including missing detailed dosage encoding and real-time updates, are discussed alongside directions for future enhancements.
Abstract (translated)
药师的角色正在从仅仅发放药物转变为在多学科医疗团队中提供全面的药学服务。这一转变的核心是获得准确、及时的药品信息，这需要强大的数据整合支持。通过利用人工智能和语义技术，知识图谱（KG）揭示了隐藏的关系，并推动基于数据的决策制定。本文介绍了medicX-KG，这是一个面向药师的知识图谱，旨在支持临床和监管决策。它构成了更广泛的medicX平台的语义层，为预测性和解释性的药房服务提供动力。 medicX-KG整合了三个来源的数据：英国国家处方集（BNF）、DrugBank以及马耳他药品管理局（MMA）。MMA解决了马耳他的监管环境问题，并结合欧洲药品管理局的对齐和部分依赖于英国供应的情况。该KG解决了缺乏统一的全国性药物仓库的问题，减少了药师对于碎片化信息来源的依赖。其设计是基于与执业药师的访谈，以确保其实用性和现实世界的适用性。 本文详细介绍了medicX-KG的构建过程，包括数据提取、本体论设计和语义映射。评估结果表明，medicX-KG有效地支持了有关药物可用性、相互作用、不良反应以及治疗类别的查询请求。文中还讨论了一些限制因素，例如缺少详细的剂量编码和实时更新，并提出了未来改进的方向。
URL
https://arxiv.org/abs/2506.17959
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Consistency Verification in Ontology-Based Process Models with Parameter Interdependencies
2025-06-19 07:21:16Tom Jeleniewski, Hamied Nabizada, Jonathan Reif, Felix Gehlhoff, Alexander FayAbstract
The formalization of process knowledge using ontologies enables consistent modeling of parameter interdependencies in manufacturing. These interdependencies are typically represented as mathematical expressions that define relations between process parameters, supporting tasks such as calculation, validation, and simulation. To support cross-context application and knowledge reuse, such expressions are often defined in a generic form and applied across multiple process contexts. This highlights the necessity of a consistent and semantically coherent model to ensure the correctness of data retrieval and interpretation. Consequently, dedicated mechanisms are required to address key challenges such as selecting context-relevant data, ensuring unit compatibility between variables and data elements, and verifying the completeness of input data required for evaluating mathematical expressions. This paper presents a set of verification mechanisms for a previously developed ontology-based process model that integrates standardized process semantics, data element definitions, and formal mathematical constructs. The approach includes (i) SPARQL-based filtering to retrieve process-relevant data, (ii) a unit consistency check based on expected-unit annotations and semantic classification, and (iii) a data completeness check to validate the evaluability of interdependencies. The applicability of the approach is demonstrated with a use case from Resin Transfer Molding (RTM), supporting the development of machine-interpretable and verifiable engineering models.
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使用本体对过程知识进行形式化，可以使制造过程中参数间相互依赖性的模型保持一致。这些相互依赖性通常以定义过程参数之间关系的数学表达式表示，支持如计算、验证和模拟等任务。为了支持跨上下文应用和知识重用，这样的表达式通常被定义为通用形式，并应用于多个过程上下文中。这突显了建立一个一致性且语义连贯模型的重要性，以确保数据检索和解释的正确性。因此，需要特定机制来解决诸如选择相关上下文数据、确保变量和数据元素之间的单位兼容性以及验证评估数学表达式所需输入数据完整性等关键挑战。 本文介绍了一套针对先前开发的基于本体的过程模型所设计的验证机制，该模型集成了标准化的过程语义、数据元素定义及形式化的数学构造。这种方法包括： (i) 基于SPARQL的数据筛选，用于检索与过程相关的信息； (ii) 一种单位一致性检查方法，基于预期单位注释和语义分类进行实施； (iii) 数据完整性检查，以验证相互依赖性的可评估性。 本文通过树脂转移模塑（RTM）的实际案例展示了该方法的应用情况，支持机器可解析且可验证的工程模型的发展。
URL
https://arxiv.org/abs/2506.16087
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Navigating the growing field of research on AI for software testing -- the taxonomy for AI-augmented software testing and an ontology-driven literature survey
Abstract
In industry, software testing is the primary method to verify and validate the functionality, performance, security, usability, and so on, of software-based systems. Test automation has gained increasing attention in industry over the last decade, following decades of intense research into test automation and model-based testing. However, designing, developing, maintaining and evolving test automation is a considerable effort. Meanwhile, AI's breakthroughs in many engineering fields are opening up new perspectives for software testing, for both manual and automated testing. This paper reviews recent research on AI augmentation in software test automation, from no automation to full automation. It also discusses new forms of testing made possible by AI. Based on this, the newly developed taxonomy, ai4st, is presented and used to classify recent research and identify open research questions.
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在工业界，软件测试是验证和确认基于软件系统的功能、性能、安全性、可用性等方面的主方法。在过去十年中，随着对测试自动化及模型驱动测试的多年深入研究，人们对测试自动化的关注日益增加。然而，设计、开发、维护和改进测试自动化需要相当大的努力。与此同时，人工智能在许多工程领域的突破为手动和自动软件测试提供了新的视角。本文回顾了近年来关于AI增强软件测试自动化的研究成果，从无自动化到全自动化，并讨论了由AI技术推动的新形式的测试方法。基于此，介绍了一种新开发的分类体系ai4st，用于对近期研究进行分类并识别出开放的研究问题。
URL
https://arxiv.org/abs/2506.14640
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Integrating Knowledge Graphs and Bayesian Networks: A Hybrid Approach for Explainable Disease Risk Prediction
2025-06-16 18:57:07Mbithe Nzomo, Deshendran MoodleyAbstract
Multimodal electronic health record (EHR) data is useful for disease risk prediction based on medical domain knowledge. However, general medical knowledge must be adapted to specific healthcare settings and patient populations to achieve practical clinical use. Additionally, risk prediction systems must handle uncertainty from incomplete data and non-deterministic health outcomes while remaining explainable. These challenges can be alleviated by the integration of knowledge graphs (KGs) and Bayesian networks (BNs). We present a novel approach for constructing BNs from ontology-based KGs and multimodal EHR data for explainable disease risk prediction. Through an application use case of atrial fibrillation and real-world EHR data, we demonstrate that the approach balances generalised medical knowledge with patient-specific context, effectively handles uncertainty, is highly explainable, and achieves good predictive performance.
Abstract (translated)
基于医疗领域知识的多模态电子健康记录（EHR）数据对于疾病风险预测非常有用。然而，通用的医学知识必须适应特定的医疗服务环境和患者群体才能实现实际临床应用。此外，风险预测系统还必须能够处理来自不完整数据和非确定性健康结果所带来的不确定性，并保持可解释性。这些挑战可以通过整合知识图谱（KGs）和贝叶斯网络（BNs）来缓解。我们提出了一种从基于本体的 KG 和多模态 EHR 数据构建 BN 的新方法，用于可解释疾病风险预测。通过心房颤动的应用案例及真实世界的 EHR 数据，我们证明了该方法能够平衡通用医学知识与患者特定背景信息，在处理不确定性方面表现出色，并具有高度的可解释性以及良好的预测性能。
URL
https://arxiv.org/abs/2506.13920
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Enhancing Omics Cohort Discovery for Research on Neurodegeneration through Ontology-Augmented Embedding Models
2025-06-16 13:27:10Jos\'e A. Pardo, Alicia G\'omez-Pascual, Jos\'e T. Palma, Juan A. Bot\'iaAbstract
The growing volume of omics and clinical data generated for neurodegenerative diseases (NDs) requires new approaches for their curation so they can be ready-to-use in bioinformatics. NeuroEmbed is an approach for the engineering of semantically accurate embedding spaces to represent cohorts and samples. The NeuroEmbed method comprises four stages: (1) extraction of ND cohorts from public repositories; (2) semi-automated normalization and augmentation of metadata of cohorts and samples using biomedical ontologies and clustering on the embedding space; (3) automated generation of a natural language question-answering (QA) dataset for cohorts and samples based on randomized combinations of standardized metadata dimensions and (4) fine-tuning of a domain-specific embedder to optimize queries. We illustrate the approach using the GEO repository and the PubMedBERT pretrained embedder. Applying NeuroEmbed, we semantically indexed 2,801 repositories and 150,924 samples. Amongst many biology-relevant categories, we normalized more than 1,700 heterogeneous tissue labels from GEO into 326 unique ontology-aligned concepts and enriched annotations with new ontology-aligned terms, leading to a fold increase in size for the metadata terms between 2.7 and 20 fold. After fine-tuning PubMedBERT with the QA training data augmented with the enlarged metadata, the model increased its mean Retrieval Precision from 0.277 to 0.866 and its mean Percentile Rank from 0.355 to 0.896. The NeuroEmbed methodology for the creation of electronic catalogues of omics cohorts and samples will foster automated bioinformatic pipelines construction. The NeuroEmbed catalogue of cohorts and samples is available at this https URL.
Abstract (translated)
神经退行性疾病（NDs）生成的组学和临床数据量的增长需要新的方法来进行数据整理，以便在生物信息学中直接使用。NeuroEmbed 是一种用于工程化语义准确嵌入空间的方法，以表示患者群体和样本。NeuroEmbed 方法包含四个阶段：(1) 从公共存储库中提取神经退行性疾病（NDs）的患者群体；(2) 使用生物医学本体论对患者群体和样本的元数据进行半自动规范化及增强，并在嵌入空间上进行聚类；(3) 根据标准化的元数据维度随机组合，自动生成用于患者群体和样本的自然语言问答（QA）数据集；以及 (4) 使用 QA 训练数据对领域特定嵌入器进行微调以优化查询。我们使用 GEO 存储库和 PubMedBERT 预训练嵌入器来演示这种方法。 通过应用 NeuroEmbed，我们为 2,801 个存储库和 150,924 个样本进行了语义索引处理。在许多生物相关的类别中，我们将来自 GEO 的超过 1,700 种不同组织标签标准化为了 326 个独特且与本体论对齐的概念，并通过添加新的与本体论对齐的术语增强了注释，这使元数据项的数量增加了 2.7 到 20 倍。 在使用扩大的元数据增强 QA 训练数据微调 PubMedBERT 后，模型的平均检索精度从 0.277 提高到 0.866，其平均百分位排名也从 0.355 提升至 0.896。 NeuroEmbed 方法用于创建组学群体和样本的电子目录，将促进自动化生物信息学管道的构建。NeuroEmbed 群体和样本目录可在此 https URL 访问。
URL
https://arxiv.org/abs/2506.13467
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Vector Ontologies as an LLM world view extraction method
2025-06-16 08:49:21Kaspar Rothenfusser, Bekk BlandoAbstract
Large Language Models (LLMs) possess intricate internal representations of the world, yet these latent structures are notoriously difficult to interpret or repurpose beyond the original prediction task. Building on our earlier work (Rothenfusser, 2025), which introduced the concept of vector ontologies as a framework for translating high-dimensional neural representations into interpretable geometric structures, this paper provides the first empirical validation of that approach. A vector ontology defines a domain-specific vector space spanned by ontologically meaningful dimensions, allowing geometric analysis of concepts and relationships within a domain. We construct an 8-dimensional vector ontology of musical genres based on Spotify audio features and test whether an LLM's internal world model of music can be consistently and accurately projected into this space. Using GPT-4o-mini, we extract genre representations through multiple natural language prompts and analyze the consistency of these projections across linguistic variations and their alignment with ground-truth data. Our results show (1) high spatial consistency of genre projections across 47 query formulations, (2) strong alignment between LLM-inferred genre locations and real-world audio feature distributions, and (3) evidence of a direct relationship between prompt phrasing and spatial shifts in the LLM's inferred vector ontology. These findings demonstrate that LLMs internalize structured, repurposable knowledge and that vector ontologies offer a promising method for extracting and analyzing this knowledge in a transparent and verifiable way.
Abstract (translated)
大型语言模型（LLM）拥有复杂的内部世界表示，然而这些潜在的结构很难被解释或在原始预测任务之外重新利用。基于我们之前的工作（Rothenfusser, 2025），该工作引入了向量本体论的概念作为将高维神经表征转换为可解释几何结构框架的基础，本文提供了对该方法的第一个实证验证。向量本体论定义了一个由本体上具有意义的维度所组成的领域特定向量空间，允许对域内概念和关系进行几何分析。我们基于Spotify音频特征构建了8维音乐流派向量本体，并测试大型语言模型内部的世界模型是否可以被一致且准确地投影到该空间中。使用GPT-4o-mini，我们通过多个自然语言提示提取音乐流派表示，并分析这些投影在语义变化下的一致性及其与实际数据的对齐情况。我们的研究结果表明： 1. 在47种查询形式下，音乐流派投射具有高空间一致性的特性。 2. 语言模型推断出的流派位置和现实世界音频特征分布之间存在强相关性。 3. 提示语句表述与大型语言模型所推测向量本体的空间移动之间存在着直接关系。 这些发现表明LLM内部化了结构化的、可重新利用的知识，而向量本体论为以透明且可验证的方式提取和分析这一知识提供了有前景的方法。
URL
https://arxiv.org/abs/2506.13252
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Cognitive Synergy Architecture: SEGO for Human-Centric Collaborative Robots
2025-06-16 07:01:56Jaehong OhAbstract
This paper presents SEGO (Semantic Graph Ontology), a cognitive mapping architecture designed to integrate geometric perception, semantic reasoning, and explanation generation into a unified framework for human-centric collaborative robotics. SEGO constructs dynamic cognitive scene graphs that represent not only the spatial configuration of the environment but also the semantic relations and ontological consistency among detected objects. The architecture seamlessly combines SLAM-based localization, deep-learning-based object detection and tracking, and ontology-driven reasoning to enable real-time, semantically coherent mapping.
Abstract (translated)
本文介绍了SEGO（语义图本体），这是一种认知映射架构，旨在将几何感知、语义推理和解释生成整合到以人为中心的协作机器人统一框架中。SEGO构建动态的认知场景图，不仅表示环境的空间配置，还表示检测到的对象之间的语义关系和本体论一致性。该架构无缝结合了基于SLAM（同步定位与地图构建）的定位、基于深度学习的目标检测和跟踪以及由本体驱动的推理，从而实现实时且语义一致的地图创建。
URL
https://arxiv.org/abs/2506.13149
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Quantum AGI: Ontological Foundations
Abstract
We examine the implications of quantum foundations for AGI, focusing on how seminal results such as Bell's theorems (non-locality), the Kochen-Specker theorem (contextuality) and no-cloning theorem problematise practical implementation of AGI in quantum settings. We introduce a novel information-theoretic taxonomy distinguishing between classical AGI and quantum AGI and show how quantum mechanics affects fundamental features of agency. We show how quantum ontology may change AGI capabilities, both via affording computational advantages and via imposing novel constraints.
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我们探讨了量子基础对AGI（通用人工智能）的影响，重点关注诸如贝尔定理（非局域性）、科赫恩-施佩克尔定理（上下文依赖性）和不可克隆定理等里程碑式成果如何在量子环境中质疑AGI的实际实现。我们引入了一个新的信息论分类法，区分了经典AGI与量子AGI，并展示了量子力学如何影响代理的基本特征。我们还展示了量子实在论如何通过提供计算优势以及施加新约束的方式改变AGI的能力。
URL
https://arxiv.org/abs/2506.13134
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Ontology Enabled Hybrid Modeling and Simulation
Abstract
We explore the role of ontologies in enhancing hybrid modeling and simulation through improved semantic rigor, model reusability, and interoperability across systems, disciplines, and tools. By distinguishing between methodological and referential ontologies, we demonstrate how these complementary approaches address interoperability challenges along three axes: Human-Human, Human-Machine, and Machine-Machine. Techniques such as competency questions, ontology design patterns, and layered strategies are highlighted for promoting shared understanding and formal precision. Integrating ontologies with Semantic Web Technologies, we showcase their dual role as descriptive domain representations and prescriptive guides for simulation construction. Four application cases - sea-level rise analysis, Industry 4.0 modeling, artificial societies for policy support, and cyber threat evaluation - illustrate the practical benefits of ontology-driven hybrid simulation workflows. We conclude by discussing challenges and opportunities in ontology-based hybrid M&S, including tool integration, semantic alignment, and support for explainable AI.
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我们探讨了本体论在通过提高语义严谨性、模型复用性和跨系统、学科和工具的互操作性来增强混合建模与仿真中的作用。通过对方法学本体论和参照本体论进行区分，我们展示了这两种互补的方法如何从人类-人类、人机和机器-机器三个维度解决互操作性挑战。通过强调能力问题、本体设计模式和技术分层策略等技术手段，促进了共享理解和形式精确度的提升。将本体与语义网络技术相结合，我们展示了它们在描述领域表示和指导仿真构建方面的双重作用。四个应用案例——海平面上升分析、工业4.0建模、支持政策的人工社会以及网络威胁评估——证明了基于本体论的混合仿真工作流程的实际优势。最后，我们讨论了基于本体论的混合建模与仿真的挑战和机遇，包括工具集成、语义对齐和支持可解释人工智能等方面。
URL
https://arxiv.org/abs/2506.12290
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Automated Validation of Textual Constraints Against AutomationML via LLMs and SHACL
2025-06-12 13:14:33Tom Westermann, Aljosha K\"ocher, Felix GehlhoffAbstract
AutomationML (AML) enables standardized data exchange in engineering, yet existing recommendations for proper AML modeling are typically formulated as informal and textual constraints. These constraints cannot be validated automatically within AML itself. This work-in-progress paper introduces a pipeline to formalize and verify such constraints. First, AML models are mapped to OWL ontologies via RML and SPARQL. In addition, a Large Language Model translates textual rules into SHACL constraints, which are then validated against the previously generated AML ontology. Finally, SHACL validation results are automatically interpreted in natural language. The approach is demonstrated on a sample AML recommendation. Results show that even complex modeling rules can be semi-automatically checked -- without requiring users to understand formal methods or ontology technologies.
Abstract (translated)
AutomationML（AML）使工程领域的数据交换标准化，然而现有的关于适当AML建模的建议通常以非正式和文本形式的约束条件呈现。这些约束无法在AML本身中自动验证。这篇正在进行中的论文介绍了一条管道来将此类约束进行形式化并加以验证。首先，通过RML（R2RML和SPARQL）将AML模型映射到OWL本体论。其次，一个大型语言模型将文本规则转换为SHACL约束条件，并将其与先前生成的AML本体论进行对比验证。最后，SHACL验证的结果自动以自然语言形式解释。该方法在一个样本AML建议上进行了演示。结果表明，即使复杂的建模规则也可以半自动检查——而无需用户理解正式方法或本体技术。
URL
https://arxiv.org/abs/2506.10678
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PlantBert: An Open Source Language Model for Plant Science
2025-06-10 15:24:03Hiba Khey, Amine Lakhder, Salma Rouichi, Imane El Ghabi, Kamal Hejjaoui, Younes En-nahli, Fahd Kalloubi, Moez AmriAbstract
The rapid advancement of transformer-based language models has catalyzed breakthroughs in biomedical and clinical natural language processing; however, plant science remains markedly underserved by such domain-adapted tools. In this work, we present PlantBert, a high-performance, open-source language model specifically tailored for extracting structured knowledge from plant stress-response literature. Built upon the DeBERTa architecture-known for its disentangled attention and robust contextual encoding-PlantBert is fine-tuned on a meticulously curated corpus of expert-annotated abstracts, with a primary focus on lentil (Lens culinaris) responses to diverse abiotic and biotic stressors. Our methodology combines transformer-based modeling with rule-enhanced linguistic post-processing and ontology-grounded entity normalization, enabling PlantBert to capture biologically meaningful relationships with precision and semantic fidelity. The underlying corpus is annotated using a hierarchical schema aligned with the Crop Ontology, encompassing molecular, physiological, biochemical, and agronomic dimensions of plant adaptation. PlantBert exhibits strong generalization capabilities across entity types and demonstrates the feasibility of robust domain adaptation in low-resource scientific fields. By providing a scalable and reproducible framework for high-resolution entity recognition, PlantBert bridges a critical gap in agricultural NLP and paves the way for intelligent, data-driven systems in plant genomics, phenomics, and agronomic knowledge discovery. Our model is publicly released to promote transparency and accelerate cross-disciplinary innovation in computational plant science.
Abstract (translated)
基于变压器的语言模型的快速进步已经推动了生物医学和临床自然语言处理领域的突破；然而，植物科学领域仍然明显缺乏此类专门化的工具支持。在此项工作中，我们介绍了PlantBert，这是一个高性能、开源的语言模型，特别针对从植物应激反应文献中提取结构化知识而设计。PlantBert基于DeBERTa架构构建，该架构以其分离的注意力机制和强大的上下文编码著称，并在经过精心策划的、由专家标注摘要组成的语料库上进行了微调，其主要关注的是豌豆（Lens culinaris）对各种非生物性和生物性应激因子的响应。我们的方法结合了基于变压器的建模与规则增强的语言后处理和本体导向的实体规范化技术，使PlantBert能够以高精度和语义准确性捕捉生物学相关的关联关系。基础语料库使用层次化方案进行了标注，该方案与作物本体相一致，涵盖了植物适应性的分子、生理学、生化及农艺维度。PlantBert在不同类型的实体中表现出强大的泛化能力，并证明了低资源科学领域稳健的领域适应性是可行的。通过提供可扩展且可重复的高分辨率实体识别框架，PlantBert填补了农业NLP中的一个关键空白，并为植物基因组学、表型组学和农艺知识发现领域的智能数据驱动系统开辟了道路。我们的模型向公众发布以促进透明度并加速计算植物科学跨学科创新的步伐。
URL
https://arxiv.org/abs/2506.08897
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Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Task
2025-06-10 15:04:28Nataliya Kosmyna, Eugene Hauptmann, Ye Tong Yuan, Jessica Situ, Xian-Hao Liao, Ashly Vivian Beresnitzky, Iris Braunstein, Pattie MaesAbstract
This study explores the neural and behavioral consequences of LLM-assisted essay writing. Participants were divided into three groups: LLM, Search Engine, and Brain-only (no tools). Each completed three sessions under the same condition. In a fourth session, LLM users were reassigned to Brain-only group (LLM-to-Brain), and Brain-only users were reassigned to LLM condition (Brain-to-LLM). A total of 54 participants took part in Sessions 1-3, with 18 completing session 4. We used electroencephalography (EEG) to assess cognitive load during essay writing, and analyzed essays using NLP, as well as scoring essays with the help from human teachers and an AI judge. Across groups, NERs, n-gram patterns, and topic ontology showed within-group homogeneity. EEG revealed significant differences in brain connectivity: Brain-only participants exhibited the strongest, most distributed networks; Search Engine users showed moderate engagement; and LLM users displayed the weakest connectivity. Cognitive activity scaled down in relation to external tool use. In session 4, LLM-to-Brain participants showed reduced alpha and beta connectivity, indicating under-engagement. Brain-to-LLM users exhibited higher memory recall and activation of occipito-parietal and prefrontal areas, similar to Search Engine users. Self-reported ownership of essays was the lowest in the LLM group and the highest in the Brain-only group. LLM users also struggled to accurately quote their own work. While LLMs offer immediate convenience, our findings highlight potential cognitive costs. Over four months, LLM users consistently underperformed at neural, linguistic, and behavioral levels. These results raise concerns about the long-term educational implications of LLM reliance and underscore the need for deeper inquiry into AI's role in learning.
Abstract (translated)
这项研究探讨了大型语言模型（LLM）辅助写作对神经和行为的影响。参与者被分为三组：LLM组、搜索引擎组和仅用大脑（不使用工具）的Brain-only组。每组在相同条件下完成了三个阶段的任务，并在第四个阶段进行了重新分组，即LLM用户转为Brain-only组（LLM-to-Brain），而Brain-only用户则被分配到LLM条件中（Brain-to-LLM）。总共有54名参与者参加了前三个阶段，其中18人完成了第四阶段。 我们使用脑电图（EEG）评估了在撰写论文时的认知负荷，并通过自然语言处理技术分析了论文内容。我们也借助人类教师和人工智能评判系统的帮助给作文打分。各组之间的命名实体识别、n-gram模式和主题语义结构显示出了内部一致性。EEG结果显示大脑连接存在显著差异：仅用大脑写作的参与者表现出最强大且分布广泛的网络；搜索引擎用户展示了中等水平的参与度；而使用LLM的用户的神经连接最为薄弱。 认知活动随着外部工具的使用而减少。在第四个阶段，由LLM转到Brain-only组的参与者显示出较低的alpha和beta波段神经连接性，这表明他们的大脑处于相对不活跃的状态。相比之下，从仅用大脑写作转换为使用LLM的人表现出更高的记忆召回率以及枕叶-顶叶和前额区域的活动水平，类似于搜索引擎用户的特征。自我报告中，LLM组参与者对其作品的拥有感最低，而Brain-only组最高。 此外，LLM用户在引用自己之前的工作时遇到了困难。虽然大型语言模型提供了即时便利性，但我们的研究结果揭示了潜在的认知成本。经过四个月的研究，使用LLM的参与者在神经、语言和行为层面均表现不佳。这些结果引发了对长期依赖于大型语言模型的教育影响的关注，并强调了需要进一步探究人工智能在学习中的角色。
URL
https://arxiv.org/abs/2506.08872
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